Energy piles represent an innovative technology that can help provide sustainable geothermal heating or cooling energy for thermal conditioning purposes. In hot-dominated climates, the interest is to inject heat in the ground and extract energy for space-c ...
Diffusion-based generative methods have proven effective in modeling trajectories with offline datasets. However, they often face computational challenges and can falter in generalization, especially in capturing temporal abstractions for long- horizon tas ...
Nuclear fusion presents a promising clean energy source to mitigate future energy crises, with magnetic confinement fusion well-positioned to provide a baseload scenario to power future reactors. The unmitigated power exhaust of such reactors threatens its ...
Modern optimization is tasked with handling applications of increasingly large scale, chiefly due to the massive amounts of widely available data and the ever-growing reach of Machine Learning. Consequently, this area of research is under steady pressure t ...
In various robotics applications, the selection of function approximation methods greatly influences the feasibility and computational efficiency of algorithms. Tensor Networks (TNs), also referred to as tensor decomposition techniques, present a versatile ...
The transition towards clean renewable energy sources, where wind and solar are prone to variation, requires adequate energy storage. Power-to-methane (PtM) systems can be part of the solution. Specifically, solid-oxide-electrolyser (SOE) based PtM systems ...
We study the problem of performance optimization of closed -loop control systems with unmodeled dynamics. Bayesian optimization (BO) has been demonstrated to be effective for improving closed -loop performance by automatically tuning controller gains or re ...
